Markets, networks, and technology

Research

I study how beliefs, financial networks, and AI shape asset prices and the broader economy.

01

Asset Pricing

How beliefs, risk, and market structure shape prices and expected returns.

02

Macro-finance

How intermediaries, financial frictions, and global imbalances shape exchange rates and capital flows.

03

AI and Skilled Work

How AI changes production, skill formation, career paths, and the organization of work.

04

Quantitative Finance

Computational and statistical methods for high-dimensional, networked financial data.

01

Working paper

International beliefs network and currencies

With M. Croce, R. Colacito, and F. Gerding

We build a multicountry model in which international intermediaries finance bilateral trade imbalances despite trading frictions. Several intermediaries may finance the same imbalance while holding different views about each country’s future capacity to service external debt.

With no arbitrage, exchange-rate adjustments depend on the entire network of intermediary expectations. Intermediaries with greater local centrality carry more weight. We test the model’s predictions for exchange rates and current-account imbalances using Consensus Economics surveys for the G-10 currencies and China from 1990 to 2023.

CurrenciesBelief networksInternational financeIntermediation
02

Current project

Artificial intelligence and the transformation of skilled work

With M. Croce and S. Raymond

We develop a macroeconomic model of how AI changes skill formation and the allocation of workers across junior and senior roles. We study the effects on production, human capital, and career progression, as well as policies that account for the social value of training, data creation, and workforce development.

Artificial intelligenceSkilled workHuman capitalCareer progressionProduction
03

Published research

A Flexible Approach to Interference Cancellation in Distributed Sensor Networks

With M. Shamsi, N. Bagheri, and F. Marvasti · IEEE Communications Letters, 2021

This paper develops a weighting method for distributed sensor networks in which noisy or malfunctioning nodes can mislead their neighbors. In diffusion LMS algorithms, the method updates combination weights using each node’s recent performance, reducing the influence of unreliable nodes as network conditions change.

Other contributions

Academic service and educational work

  • Academic refereeEconomics Letters (Elsevier)
  • Solutions manualHigh-Dimensional Statistics: A Non-Asymptotic Viewpoint by Martin J. Wainwright (unpublished)